Understanding electric vehicle ownership using data fusion and spatial modeling

Meiyu (Melrose) Pan, Majbah Uddin, Hyeonsup Lim

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The global shift toward electric vehicles (EVs) for climate sustainability lacks comprehensive insights into the impact of the built environment on EV ownership, especially in varying spatial contexts. This study, focusing on New York State, integrates data fusion techniques across diverse datasets to examine the influence of socioeconomic and built environmental factors on EV ownership. The utilization of spatial regression models reveals consistent coefficient values, highlighting the robustness of the results, with the Spatial Lag model better at capturing spatial autocorrelation. Results underscore the significance of charging stations within a 10-mile radius, indicative of a preference for convenient charging options influencing EV ownership decisions. Factors like higher education levels, lower rental populations, and concentrations of older population align with increased EV ownership. Utilizing publicly available data offers a more accessible avenue for understanding EV ownership across regions, complementing traditional survey approaches.

Original languageEnglish
Article number104075
JournalTransportation Research Part D: Transport and Environment
Volume127
DOIs
StatePublished - Feb 2024

Funding

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://energy.gov/downloads/doe-public-access-plan ). The authors would like to acknowledge the support of the New York State Department of Transportation (NYSDOT). The opinions, findings, and conclusions in this paper are those of the authors and not necessarily those of the NYSDOT.

Keywords

  • Built environment
  • Charging stations
  • Data fusion
  • Electric vehicle
  • Public data
  • Spatial Regression

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